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What’s the Main Priority for Data Labeling in Modern ML, Quality or Scale: Experts Weigh In
AI relies on data labeling for training algorithms – without the data, there can’t be any machine learning. Data labeling accuracy is often difficult to achieve on its own, but it becomes even more of an issue when scalability is at stake. It’s believed by many... Read more
6 Business-Friendly Data Analysis Solutions
Nowadays, you may hear about big data and machines taking over the lives of people worldwide. From artificial intelligence (AI) to coding to computer science, and so on, technology has ushered in miracles and advancements that might not have been anticipated years ago. In addition, various... Read more
Systems built with software can be fragile. While the software is highly predictable, the runtime context can provide unexpected inputs and situations. Devices fail, networks are unreliable, mere anarchy is loosed on our application. We need to have a way to work around the spectrum of... Read more
The Rapid Evolution of the Canonical Stack for Machine Learning
Just a few years ago, almost nobody was building software to support the surge of new machine learning apps coming into production all over the world.  Every big tech company, like Google, Lyft, Microsoft, and Amazon rolled their own AI/ML tech stack from scratch.  See the... Read more
Top 5 Applications of Machine Learning in Healthcare
Machine learning (ML) is a branch of artificial intelligence (AI), where computer systems independently find solutions to complex problems using recurring patterns in databases. Put differently, machine learning helps IT systems to recognize patterns from existing algorithms and datasets, then go ahead and develop appropriate solutions.... Read more
Decoupling Complex Systems with Event Driven Python Programming
We often think about events as ordered points in time that happen one after another, often with some kind of cause-effect relationship. But, in programming, events are often understood a bit differently. They are not necessarily “things that happen.” Events in programming are more often understood... Read more
Concept Drift 101
Our world is constantly changing and what’s considered true and obvious today may not be so tomorrow. This is particularly true in the world of machine learning models. The objective of machine learning models is to extract patterns from past data and use them to predict... Read more
On The Dangers of Stochastic Parrots: Can Language Models Be Too Big?
In March this year, the ACM FAccT conference published the paper titled above, authored by Emily Bender, Timnit Gebru, Angelina McMillan-Major, and “Shmargaret Shmitchell”. This paper attracted intense controversy, and led to the exit of Gebru and Margaret Mitchell (“Shmargaret Shmitchell” in the paper) from the Google AI Ethics team.... Read more
Take the Data Science and Machine Learning Survey
As a practicing data professional, you are in a unique position to help the world better understand the Data Science and Machine Learning landscape. Toward that end, Bob E. Hayes, PhD of Business Over Broadway is conducting a worldwide survey of data professionals; this survey will... Read more
Why Use D3 for Data Visualization?
This is like saying why eat burritos? Because they’re amazing!!! That’s why!!! OK, now some of you may be saying to yourselves, “Bill, I don’t like burritos. You’ve lost me.” First, I’m very sorry for you. Not appreciating burritos may be genetic and I won’t judge... Read more